1. Field
The present disclosure relates generally to identifying objects in images and, in particular, to identifying objects in images by detecting features representing the objects in the images. Still more particularly, the present disclosure relates to a method and apparatus for performing isotropic feature detection using sectioned structures to identify objects of interest in images.
2. Background
Object detection is the detection of objects of interest in images and/or video. An object of interest may be, for example, a building, a landscape element, a terrain element, an aerial vehicle, a ground vehicle, a missile, a spacecraft, or some other type of object. In this manner, an object of interest may be a natural or manmade object of interest.
Object detection is a fundamental part of performing many different types of operations. For example, object detection may be used when performing surveillance operations, target tracking operations, target acquisition operations, target identification operations, and/or other types of operations.
Object detection oftentimes includes performing feature detection. Feature detection is the detection of features of interest within an image. As used herein, a “feature,” may be some type of discernible element or structure within an image. For example, a feature may be a point, an edge, or a shape in an image that represents an object or some portion of an object.
In one illustrative example, an optical tracking system may be used to generate images of a target object. Feature detection may be used to identify the target object in the generated images based on the detection of features that represent the target object in the generated images. In this example, the ability to process images and detect features of interest quickly and efficiently may be crucial. Consequently, a method for feature detection that is not computationally intensive or time-consuming may be desired.
Feature detection oftentimes includes edge detection. Edge detection is the detection of features in the form of edges. Oftentimes, with edge detection, an image, such as a camera image, is converted into a binary image in which pixels having one value represent a background and pixels having a another value represent edges. These binary images are also referred to as edge images. Pattern matching is then used to match the characteristics of the edges in an edge image with known patterns of edges for certain objects of interest.
Using edge imagery may be beneficial because edge imagery may be relatively insensitive to changes in lighting. For example, the edges of an object may appear the same in an edge image whether the object of interest is brighter or darker than the background in the scene as long as the object of interest has distinctive edges.
However, with some currently available methods of edge detection and pattern matching, analyzing images of a scene may take more time than desired for real-time applications. When using edge images, a pixel-by-pixel search may need to be made for locations in the image corresponding to a best-fit for an object of interest. The fit may be made by matching a template of an edge pattern that represents the object of interest to a portion of the edges in an edge image. This template may be referred to as an edge pattern template or in some cases, a wireframe template.
Further, rotational adjustments may need to be made to take into account the different possible orientations of a scene relative to the imaging system generating the images of the scene. Still further, the range of a scene with respect to an imaging system may affect the positions, shapes, and/or sizes of edges that are detected. For example, the edges detected for images of terrain taken at higher elevations may be fewer than the edges detected for images taken at lower elevations.
Creating an edge pattern template that accounts for different ranges may be more difficult than desired. Using a single edge pattern template for edge images corresponding to different ranges may reduce the overall accuracy with which objects of interest are detected and located. Therefore, it would be desirable to have a method and apparatus that take into account at least some of the issues discussed above, as well as other possible issues.